Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model

Abstract Human drivers conduct compensatory behaviour to counteract the increased risk while being distracted. This kind of compensation strategy should be learned for a safer and smart design of adaptive cruise control system (ACC). Hence, a simulator study was conducted, requiring performance visu...

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Main Authors: Penghui Li, Mengxia Hu, Feng Chen, Chengliang Xu, Chunjiao Dong, Yibing Li
Format: Article
Language:English
Published: Wiley 2021-10-01
Series:IET Intelligent Transport Systems
Subjects:
Online Access:https://doi.org/10.1049/itr2.12092
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author Penghui Li
Mengxia Hu
Feng Chen
Chengliang Xu
Chunjiao Dong
Yibing Li
author_facet Penghui Li
Mengxia Hu
Feng Chen
Chengliang Xu
Chunjiao Dong
Yibing Li
author_sort Penghui Li
collection DOAJ
description Abstract Human drivers conduct compensatory behaviour to counteract the increased risk while being distracted. This kind of compensation strategy should be learned for a safer and smart design of adaptive cruise control system (ACC). Hence, a simulator study was conducted, requiring performance visual, cognitive, and combined secondary tasks during a car following scenario. An increased time headway (THW) was found in all of the three distraction conditions, which confirmed that drivers compensated an extra THW to counteract the increased crash risk. Furthermore, crash probability models using binary logistic regression with random intercept were constructed where driver distraction and dynamic traffic situations were embodied as inputs. Results showed that crash risk increased with reduced THW, increased lead vehicle deceleration, and unopened brake light of the lead vehicle. Besides, visual‐related distractions increased crash risk, while pure cognitive distraction lowered crash risk in low THW (lower than 1.8 s) condition and increased crash risk in high THW (larger than 1.8 s) condition. Based on the authors' proposed models, theoretical compensation in THW to fully counteract the increased crash risk by distraction was derived, which could be used for the design of a human‐like ACC with automatic adjustment of THW setting considering driver distraction.
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spelling doaj.art-9e87dc851104476ba562f27f9c6388752022-12-22T03:17:21ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-10-0115101241125510.1049/itr2.12092Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk modelPenghui Li0Mengxia Hu1Feng Chen2Chengliang Xu3Chunjiao Dong4Yibing Li5State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility Tsinghua University Beijing ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility Tsinghua University Beijing ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University Shanghai ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility Tsinghua University Beijing ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility Tsinghua University Beijing ChinaAbstract Human drivers conduct compensatory behaviour to counteract the increased risk while being distracted. This kind of compensation strategy should be learned for a safer and smart design of adaptive cruise control system (ACC). Hence, a simulator study was conducted, requiring performance visual, cognitive, and combined secondary tasks during a car following scenario. An increased time headway (THW) was found in all of the three distraction conditions, which confirmed that drivers compensated an extra THW to counteract the increased crash risk. Furthermore, crash probability models using binary logistic regression with random intercept were constructed where driver distraction and dynamic traffic situations were embodied as inputs. Results showed that crash risk increased with reduced THW, increased lead vehicle deceleration, and unopened brake light of the lead vehicle. Besides, visual‐related distractions increased crash risk, while pure cognitive distraction lowered crash risk in low THW (lower than 1.8 s) condition and increased crash risk in high THW (larger than 1.8 s) condition. Based on the authors' proposed models, theoretical compensation in THW to fully counteract the increased crash risk by distraction was derived, which could be used for the design of a human‐like ACC with automatic adjustment of THW setting considering driver distraction.https://doi.org/10.1049/itr2.12092Velocity, acceleration and rotation controlRoad‐traffic system controlRegression analysisSelf‐adjusting control systems
spellingShingle Penghui Li
Mengxia Hu
Feng Chen
Chengliang Xu
Chunjiao Dong
Yibing Li
Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model
IET Intelligent Transport Systems
Velocity, acceleration and rotation control
Road‐traffic system control
Regression analysis
Self‐adjusting control systems
title Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model
title_full Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model
title_fullStr Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model
title_full_unstemmed Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model
title_short Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model
title_sort improve adaptive cruise control under driver distraction time headway compensation based on a random effect crash risk model
topic Velocity, acceleration and rotation control
Road‐traffic system control
Regression analysis
Self‐adjusting control systems
url https://doi.org/10.1049/itr2.12092
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AT fengchen improveadaptivecruisecontrolunderdriverdistractiontimeheadwaycompensationbasedonarandomeffectcrashriskmodel
AT chengliangxu improveadaptivecruisecontrolunderdriverdistractiontimeheadwaycompensationbasedonarandomeffectcrashriskmodel
AT chunjiaodong improveadaptivecruisecontrolunderdriverdistractiontimeheadwaycompensationbasedonarandomeffectcrashriskmodel
AT yibingli improveadaptivecruisecontrolunderdriverdistractiontimeheadwaycompensationbasedonarandomeffectcrashriskmodel